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Microfounded forecasting

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  • Gaglianone, Wagner Piazza
  • Issler, João Victor

Abstract

This paper proposes a Önancial approach to economic forecasting which can be applied to data bases of surveys of forecasts. We model the forecasting decision of an individual from Örst principles (i.e., microfounded) and show that surveys of forecasts obey an a¢ ne factor structure with a single factor which is the conditional expectation of the target variable based on common information (public and private). This holds in a context where individuals have access to public information and also have access to private information with common and idiosyncratic components. We show that asymptotically e¢ cient forecasts of the target variable can be built using the generalized method of moments in a panel-data context, when N and T diverge or when T diverges with N Öxed. In this context, the optimal forecast is a function of the consensus forecast of the survey (a cross-sectional average of survey forecasts) after appropriately Öltering out two bias terms. This links the Önancial approach of economic forecasting to the forecast-combination literature, where idiosyncratic risk of individual forecasts can be diversiÖed out. Our microfounded approach is applied to a world-class data base on surveys of expectations and the techniques advanced here fare best when compared with competitive alternatives.

Suggested Citation

  • Gaglianone, Wagner Piazza & Issler, João Victor, 2019. "Microfounded forecasting," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 813, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
  • Handle: RePEc:fgv:epgewp:813
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    3. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).

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